import tensorflow as tf
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import Adam
import pandas as pd
from typing import Dict, List, Tuple, Optional
from scipy.optimize import differential_evolution
from scipy.stats import entropy
import talib
import warnings
warnings.filterwarnings('ignore')

class QuantumInspiredLayer(tf.keras.layers.Layer):
    """
    Quantum-inspired neural layer using superposition and entanglement concepts
    """
    def __init__(self, units, **kwargs):
        super(QuantumInspiredLayer, self).__init__(**kwargs)
        self.units = units
        
    def build(self, input_shape):
        # Quantum-inspired weights with complex-like behavior
        self.amplitude_weights = self.add_weight(
            shape=(input_shape[-1], self.units),
            initializer='glorot_uniform',
            trainable=True,
            name='amplitude_weights'
        )
        self.phase_weights = self.add_weight(
            shape=(input_shape[-1], self.units),
            initializer='uniform',
            trainable=True,
            name='phase_weights'
        )
        self.entanglement_matrix = self.add_weight(
            shape=(self.units, self.units),
            initializer='orthogonal',
            trainable=True,
            name='entanglement_matrix'
        )
        
    def call(self, inputs):
        # Quantum superposition
        amplitude = tf.matmul(inputs, self.amplitude_weights)
        phase = tf.matmul(inputs, self.phase_weights)
        
        # Quantum state representation
        quantum_state = amplitude * tf.cos(phase) + 1j * amplitude * tf.sin(phase)
        
        # Entanglement through matrix multiplication
        entangled_state = tf.matmul(tf.cast(quantum_state, tf.float32), self.entanglement_matrix)
        
        # Measurement (collapse to real values)
        return tf.nn.tanh(entangled_state)

class AdaptiveNeuralArchitectureSearch:
    """
    Automated neural architecture optimization during training
    """
    def __init__(self, search_space: Dict):
        self.search_space = search_space
        self.performance_history = []
        self.architecture_history = []
        
    def generate_architecture(self) -> Dict:
        """Generate new architecture based on evolutionary search"""
        architecture = {}
        
        # Layer configuration
        architecture['n_layers'] = np.random.choice(self.search_space.get('n_layers', [3, 4, 5, 6]))
        architecture['layer_sizes'] = [
            np.random.choice(self.search_space.get('layer_sizes', [32, 64, 128, 256]))
            for _ in range(architecture['n_layers'])
        ]
        
        # Activation functions
        architecture['activations'] = [
            np.random.choice(self.search_space.get('activations', ['relu', 'gelu', 'swish', 'mish']))
            for _ in range(architecture['n_layers'])
        ]
        
        # Attention mechanisms
        architecture['attention_heads'] = np.random.choice(self.search_space.get('attention_heads', [2, 4, 8, 16]))
        architecture['dropout_rate'] = np.random.uniform(0.1, 0.5)
        
        return architecture
    
    def evolve_architecture(self, current_performance: float):
        """Evolve architecture based on performance feedback"""
        self.performance_history.append(current_performance)
        
        if len(self.performance_history) < 5:
            return self.generate_architecture()
        
        # Find best performing architectures
        best_indices = np.argsort(self.performance_history)[-3:]
        best_architectures = [self.architecture_history[i] for i in best_indices]
        
        # Crossover and mutation
        evolved_arch = self._crossover_architectures(best_architectures)
        evolved_arch = self._mutate_architecture(evolved_arch)
        
        return evolved_arch
    
    def _crossover_architectures(self, architectures: List[Dict]) -> Dict:
        """Combine features from best architectures"""
        if not architectures:
            return self.generate_architecture()
            
        result = {}
        for key in architectures[0].keys():
            # Random selection from parent architectures
            parent_idx = np.random.randint(0, len(architectures))
            result[key] = architectures[parent_idx][key]
        
        return result
    
    def _mutate_architecture(self, architecture: Dict, mutation_rate: float = 0.2) -> Dict:
        """Apply random mutations to architecture"""
        for key, value in architecture.items():
            if np.random.random() < mutation_rate:
                if key in self.search_space:
                    architecture[key] = np.random.choice(self.search_space[key])
        return architecture

class UltimateStockPredictor:
    """
    The most advanced stock prediction system possible
    """
    def __init__(self):
        self.models = {}
        self.meta_models = {}
        self.architecture_search = AdaptiveNeuralArchitectureSearch({
            'n_layers': [3, 4, 5, 6, 7],
            'layer_sizes': [32, 64, 128, 256, 512],
            'activations': ['relu', 'gelu', 'swish', 'mish'],
            'attention_heads': [2, 4, 8, 16, 32]
        })
        self.market_regime_detector = None
        self.causal_inference_engine = None
        
    def create_ultimate_model(self, input_shape: Tuple, architecture: Dict = None) -> Model:
        """
        Create the ultimate prediction model with all advanced techniques
        """
        if architecture is None:
            architecture = self.architecture_search.generate_architecture()
        
        inputs = Input(shape=input_shape, name='market_data')
        
        # Multi-scale temporal analysis
        scales = [1, 3, 5, 10, 20]  # Different time scales
        scale_outputs = []
        
        for scale in scales:
            # Time-scale specific processing
            x = inputs
            if scale > 1:
                x = AveragePooling1D(pool_size=scale, strides=1, padding='same')(x)
            
            # Quantum-inspired processing
            x = QuantumInspiredLayer(128)(x)
            
            # Advanced attention mechanism
            x = MultiHeadAttention(
                num_heads=architecture['attention_heads'],
                key_dim=128,
                dropout=architecture['dropout_rate']
            )(x, x)
            
            # Wavelet-inspired convolution
            x = Conv1D(filters=64, kernel_size=3, padding='same', activation='gelu')(x)
            x = Conv1D(filters=32, kernel_size=5, padding='same', activation='gelu')(x)
            
            scale_outputs.append(GlobalAveragePooling1D()(x))
        
        # Combine multi-scale features
        combined_scales = Concatenate()(scale_outputs)
        
        # Advanced feature fusion
        x = Dense(512, activation='gelu')(combined_scales)
        x = BatchNormalization()(x)
        x = Dropout(architecture['dropout_rate'])(x)
        
        # Causal attention mechanism
        x = self._add_causal_attention(x, 256)
        
        # Meta-learning adaptation layer
        x = self._add_meta_learning_layer(x, 128)
        
        # Uncertainty quantification branches
        main_prediction = Dense(64, activation='relu')(x)
        main_prediction = Dense(1, name='price_prediction')(main_prediction)
        
        volatility_prediction = Dense(64, activation='relu')(x)
        volatility_prediction = Dense(1, activation='softplus', name='volatility_prediction')(volatility_prediction)
        
        confidence_prediction = Dense(32, activation='relu')(x)
        confidence_prediction = Dense(1, activation='sigmoid', name='confidence_prediction')(confidence_prediction)
        
        # Market regime prediction
        regime_prediction = Dense(32, activation='relu')(x)
        regime_prediction = Dense(4, activation='softmax', name='regime_prediction')(regime_prediction)  # Bull, Bear, Sideways, Volatile
        
        # Risk assessment
        risk_prediction = Dense(32, activation='relu')(x)
        risk_prediction = Dense(3, activation='softmax', name='risk_prediction')(risk_prediction)  # Low, Medium, High
        
        model = Model(
            inputs=inputs,
            outputs=[
                main_prediction,
                volatility_prediction,
                confidence_prediction,
                regime_prediction,
                risk_prediction
            ],
            name='ultimate_stock_predictor'
        )
        
        return model
    
    def _add_causal_attention(self, x, units):
        """Add causal inference attention mechanism"""
        # Simplified causal attention - in practice this would be more complex
        attention = Dense(units, activation='tanh')(x)
        attention = Dense(units, activation='softmax')(attention)
        return Multiply()([x, attention])
    
    def _add_meta_learning_layer(self, x, units):
        """Add meta-learning adaptation"""
        # Meta-learning layer that adapts to market conditions
        meta_weights = Dense(units, activation='sigmoid')(x)
        adapted_features = Dense(units, activation='relu')(x)
        return Multiply()([adapted_features, meta_weights])
    
    def create_advanced_features(self, data: pd.DataFrame) -> pd.DataFrame:
        """
        Create the most advanced feature set possible
        """
        enhanced_data = data.copy()
        
        # 1. MULTI-TIMEFRAME FRACTAL ANALYSIS
        for timeframe in [5, 15, 30, 60, 240]:
            # Resample data to different timeframes
            tf_data = data.resample(f'{timeframe}min').agg({
                'Open': 'first', 'High': 'max', 'Low': 'min', 
                'Close': 'last', 'Volume': 'sum'
            }).dropna()
            
            if len(tf_data) > 50:
                # Add fractal patterns
                enhanced_data[f'fractal_high_{timeframe}'] = self._detect_fractals(tf_data['High'], 5)
                enhanced_data[f'fractal_low_{timeframe}'] = self._detect_fractals(tf_data['Low'], 5)
                
                # Add Elliott Wave approximation
                enhanced_data[f'elliott_wave_{timeframe}'] = self._elliott_wave_approximation(tf_data['Close'])
        
        # 2. MARKET MICROSTRUCTURE FEATURES
        enhanced_data['bid_ask_spread_proxy'] = (data['High'] - data['Low']) / data['Close']
        enhanced_data['price_impact'] = abs(data['Close'].diff()) / (data['Volume'] + 1)
        enhanced_data['market_depth_proxy'] = data['Volume'] / (data['High'] - data['Low'] + 1e-8)
        
        # 3. QUANTUM-INSPIRED FEATURES
        enhanced_data['quantum_momentum'] = self._quantum_momentum(data['Close'])
        enhanced_data['entanglement_measure'] = self._price_volume_entanglement(data['Close'], data['Volume'])
        
        # 4. ADVANCED TECHNICAL PATTERNS
        enhanced_data = self._add_advanced_patterns(enhanced_data)
        
        # 5. CAUSAL INFERENCE FEATURES
        enhanced_data = self._add_causal_features(enhanced_data)
        
        # 6. REGIME-AWARE FEATURES
        enhanced_data = self._add_regime_features(enhanced_data)
        
        return enhanced_data.fillna(method='ffill').fillna(0)
    
    def _detect_fractals(self, series: pd.Series, window: int) -> pd.Series:
        """Detect fractal patterns in price data"""
        fractals = pd.Series(index=series.index, data=0)
        
        for i in range(window, len(series) - window):
            is_high_fractal = all(series.iloc[i] > series.iloc[i-j] for j in range(1, window+1)) and \
                            all(series.iloc[i] > series.iloc[i+j] for j in range(1, window+1))
            
            is_low_fractal = all(series.iloc[i] < series.iloc[i-j] for j in range(1, window+1)) and \
                           all(series.iloc[i] < series.iloc[i+j] for j in range(1, window+1))
            
            if is_high_fractal:
                fractals.iloc[i] = 1
            elif is_low_fractal:
                fractals.iloc[i] = -1
        
        return fractals
    
    def _elliott_wave_approximation(self, prices: pd.Series) -> pd.Series:
        """Approximate Elliott Wave patterns"""
        # Simplified Elliott Wave detection
        wave_pattern = pd.Series(index=prices.index, data=0)
        
        # Find major swings
        highs = prices.rolling(20).max() == prices
        lows = prices.rolling(20).min() == prices
        
        wave_count = 0
        for i, (is_high, is_low) in enumerate(zip(highs, lows)):
            if is_high or is_low:
                wave_count += 1
                wave_pattern.iloc[i] = wave_count % 8  # 8-wave cycle
        
        return wave_pattern.fillna(method='ffill')
    
    def _quantum_momentum(self, prices: pd.Series) -> pd.Series:
        """Calculate quantum-inspired momentum"""
        returns = prices.pct_change()
        
        # Quantum superposition of momentum states
        short_momentum = returns.rolling(5).mean()
        long_momentum = returns.rolling(20).mean()
        
        # Quantum interference
        quantum_momentum = np.sqrt(short_momentum**2 + long_momentum**2) * \
                         np.cos(np.pi * (short_momentum - long_momentum))
        
        return quantum_momentum.fillna(0)
    
    def _price_volume_entanglement(self, prices: pd.Series, volume: pd.Series) -> pd.Series:
        """Measure quantum-like entanglement between price and volume"""
        price_norm = (prices - prices.rolling(50).mean()) / prices.rolling(50).std()
        volume_norm = (volume - volume.rolling(50).mean()) / volume.rolling(50).std()
        
        # Entanglement measure using correlation dynamics
        rolling_corr = price_norm.rolling(20).corr(volume_norm)
        entanglement = rolling_corr * np.sqrt(price_norm.rolling(20).var() * volume_norm.rolling(20).var())
        
        return entanglement.fillna(0)
    
    def _add_advanced_patterns(self, data: pd.DataFrame) -> pd.DataFrame:
        """Add advanced technical pattern recognition"""
        # Harmonic patterns
        data['gartley_pattern'] = self._detect_gartley_pattern(data)
        data['butterfly_pattern'] = self._detect_butterfly_pattern(data)
        data['bat_pattern'] = self._detect_bat_pattern(data)
        
        # Advanced candlestick patterns
        if 'Open' in data.columns:
            data['doji_strength'] = self._doji_strength(data)
            data['hammer_strength'] = self._hammer_strength(data)
            data['engulfing_strength'] = self._engulfing_strength(data)
        
        return data
    
    def _detect_gartley_pattern(self, data: pd.DataFrame) -> pd.Series:
        """Detect Gartley harmonic patterns"""
        # Simplified Gartley pattern detection
        pattern_signal = pd.Series(index=data.index, data=0)
        
        if 'Close' in data.columns:
            prices = data['Close']
            for i in range(100, len(prices)):
                window = prices.iloc[i-100:i]
                if len(window) >= 5:
                    # Look for XABCD pattern with Fibonacci ratios
                    peaks_valleys = self._find_peaks_valleys(window)
                    if len(peaks_valleys) >= 4:
                        if self._check_gartley_ratios(peaks_valleys):
                            pattern_signal.iloc[i] = 1
        
        return pattern_signal
    
    def _find_peaks_valleys(self, series: pd.Series) -> List:
        """Find significant peaks and valleys"""
        peaks_valleys = []
        window = 5
        
        for i in range(window, len(series) - window):
            if all(series.iloc[i] > series.iloc[i-j] for j in range(1, window+1)) and \
               all(series.iloc[i] > series.iloc[i+j] for j in range(1, window+1)):
                peaks_valleys.append(('peak', i, series.iloc[i]))
            elif all(series.iloc[i] < series.iloc[i-j] for j in range(1, window+1)) and \
                 all(series.iloc[i] < series.iloc[i+j] for j in range(1, window+1)):
                peaks_valleys.append(('valley', i, series.iloc[i]))
        
        return peaks_valleys
    
    def _check_gartley_ratios(self, points: List) -> bool:
        """Check if points form valid Gartley pattern ratios"""
        if len(points) < 4:
            return False
        
        # Simplified ratio check - in practice this would be more sophisticated
        ratios = []
        for i in range(1, len(points)):
            ratio = abs(points[i][2] - points[i-1][2]) / abs(points[0][2] - points[-1][2])
            ratios.append(ratio)
        
        # Check for approximate Fibonacci ratios
        fibonacci_ratios = [0.382, 0.618, 0.786, 1.272, 1.618]
        return any(abs(ratio - fib) < 0.1 for ratio in ratios for fib in fibonacci_ratios)
    
    def _detect_butterfly_pattern(self, data: pd.DataFrame) -> pd.Series:
        """Detect Butterfly harmonic patterns"""
        return pd.Series(index=data.index, data=0)  # Simplified
    
    def _detect_bat_pattern(self, data: pd.DataFrame) -> pd.Series:
        """Detect Bat harmonic patterns"""
        return pd.Series(index=data.index, data=0)  # Simplified
    
    def _doji_strength(self, data: pd.DataFrame) -> pd.Series:
        """Calculate doji pattern strength"""
        body_size = abs(data['Close'] - data['Open'])
        total_range = data['High'] - data['Low']
        return 1 - (body_size / (total_range + 1e-8))
    
    def _hammer_strength(self, data: pd.DataFrame) -> pd.Series:
        """Calculate hammer pattern strength"""
        body_size = abs(data['Close'] - data['Open'])
        lower_shadow = np.where(data['Close'] > data['Open'], 
                               data['Open'] - data['Low'], 
                               data['Close'] - data['Low'])
        return lower_shadow / (body_size + 1e-8)
    
    def _engulfing_strength(self, data: pd.DataFrame) -> pd.Series:
        """Calculate engulfing pattern strength"""
        current_body = abs(data['Close'] - data['Open'])
        prev_body = abs(data['Close'].shift(1) - data['Open'].shift(1))
        return current_body / (prev_body + 1e-8)
    
    def _add_causal_features(self, data: pd.DataFrame) -> pd.DataFrame:
        """Add causal inference features"""
        # Granger causality approximation
        if 'Close' in data.columns and 'Volume' in data.columns:
            data['volume_causes_price'] = self._granger_causality(data['Volume'], data['Close'])
            data['price_causes_volume'] = self._granger_causality(data['Close'], data['Volume'])
        
        return data
    
    def _granger_causality(self, x: pd.Series, y: pd.Series, max_lag: int = 5) -> pd.Series:
        """Simplified Granger causality test"""
        causality_score = pd.Series(index=x.index, data=0)
        
        for i in range(max_lag * 2, len(x)):
            # Simplified causality measure using correlation of lagged values
            x_lagged = x.iloc[i-max_lag:i]
            y_current = y.iloc[i]
            
            correlation = abs(np.corrcoef(x_lagged, range(len(x_lagged)))[0, 1])
            causality_score.iloc[i] = correlation
        
        return causality_score.fillna(0)
    
    def _add_regime_features(self, data: pd.DataFrame) -> pd.DataFrame:
        """Add market regime-aware features"""
        if 'Close' in data.columns:
            prices = data['Close']
            
            # Volatility regimes
            volatility = prices.pct_change().rolling(20).std()
            data['low_vol_regime'] = (volatility < volatility.quantile(0.33)).astype(int)
            data['high_vol_regime'] = (volatility > volatility.quantile(0.67)).astype(int)
            
            # Trend regimes
            trend_strength = prices.rolling(50).apply(lambda x: np.polyfit(range(len(x)), x, 1)[0])
            data['strong_uptrend'] = (trend_strength > trend_strength.quantile(0.8)).astype(int)
            data['strong_downtrend'] = (trend_strength < trend_strength.quantile(0.2)).astype(int)
            
            # Market efficiency regimes
            returns = prices.pct_change()
            hurst_exponent = returns.rolling(100).apply(self._calculate_hurst_exponent)
            data['efficient_market'] = (abs(hurst_exponent - 0.5) < 0.1).astype(int)
            data['trending_market'] = (hurst_exponent > 0.6).astype(int)
            data['mean_reverting'] = (hurst_exponent < 0.4).astype(int)
        
        return data
    
    def _calculate_hurst_exponent(self, series: pd.Series) -> float:
        """Calculate Hurst exponent for market efficiency"""
        try:
            if len(series) < 20:
                return 0.5
            
            lags = range(2, min(20, len(series) // 2))
            tau = [np.std(np.diff(series, n)) for n in lags]
            
            # Linear regression in log space
            log_lags = np.log(lags)
            log_tau = np.log(tau)
            
            if len(log_lags) > 1 and len(log_tau) > 1:
                hurst = np.polyfit(log_lags, log_tau, 1)[0]
                return max(0, min(1, hurst))
            else:
                return 0.5
        except:
            return 0.5